Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation
| Autor: | Emelia J. Benjamin, Veikko Salomaa, Lu-Chen Weng, Kathryn L. Lunetta, Christopher D. Anderson, Brandon K. Fornwalt, Samuli Ripatti, Christopher M. Haggerty, Qiuxi Huang, Steven A. Lubitz, Shaan Khurshid, Dustin N. Hartzel, Ludovic Trinquart, Jeffrey M. Ashburner, Patrick T. Ellinor, Nina Mars |
|---|---|
| Přispěvatelé: | Institute for Molecular Medicine Finland, Complex Disease Genetics, Helsinki Institute of Life Science HiLIFE, Centre of Excellence in Complex Disease Genetics, Department of Public Health, Samuli Olli Ripatti / Principal Investigator, Faculty Common Matters (Faculty of Social Sciences), Biostatistics Helsinki |
| Rok vydání: | 2021 |
| Předmět: |
Male
medicine.medical_specialty 030204 cardiovascular system & hematology VALIDATION Article 03 medical and health sciences 0302 clinical medicine Risk Factors Internal medicine Atrial Fibrillation genomics MANAGEMENT medicine FAILURE Humans 030212 general & internal medicine Genetic risk COMMON Stroke genetic predisposition to disease CURVE Models Genetic business.industry aging NATIONAL HEART 1184 Genetics developmental biology physiology Age Factors Models Cardiovascular risk assessment Atrial fibrillation ASSOCIATION General Medicine Guideline Middle Aged medicine.disease 3121 General medicine internal medicine and other clinical medicine ONSET SURVIVAL Cardiology Female Risk assessment business STROKE |
| Zdroj: | Circ Genom Precis Med |
| ISSN: | 2574-8300 |
| Popis: | Background: Atrial fibrillation (AF) risk estimation using clinical factors with or without genetic information may identify AF screening candidates more accurately than the guideline-based age threshold of ≥65 years. Methods: We analyzed 4 samples across the United States and Europe (derivation: UK Biobank; validation: FINRISK, Geisinger MyCode Initiative, and Framingham Heart Study). We estimated AF risk using the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) score and a combination of CHARGE-AF and a 1168-variant polygenic score (Predict-AF). We compared the utility of age, CHARGE-AF, and Predict-AF for predicting 5-year AF by quantifying discrimination and calibration. Results: Among 543 093 individuals, 8940 developed AF within 5 years. In the validation sets, CHARGE-AF (C index range, 0.720–0.824) and Predict-AF (0.749–0.831) had largely comparable discrimination, both favorable to continuous age (0.675–0.801). Calibration was similar using CHARGE-AF (slope range, 0.67–0.87) and Predict-AF (0.65–0.83). Net reclassification improvement using Predict-AF versus CHARGE-AF was modest (net reclassification improvement range, 0.024–0.057) but more favorable among individuals aged Conclusions: AF risk estimation using clinical factors may prioritize individuals for AF screening more precisely than the age threshold endorsed in current guidelines. The additional value of genetic predisposition is modest but greatest among younger individuals. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|